In [1]:
import pandas as pd
import pandas_profiling as pp 
import numpy as np
import sklearn 
from sklearn.datasets import load_boston
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import warnings
warnings.filterwarnings('ignore')
np.random.seed(23)
In [2]:
fires_df = pd.read_csv('forest_fires_dataset.csv')
fires_df
Out[2]:
X Y month day FFMC DMC DC ISI temp RH wind rain area
0 7 5 mar fri 86.2 26.2 94.3 5.1 8.2 51.0 6.7 0.0 0.00
1 7 4 oct tue 90.6 35.4 669.1 6.7 18.0 33.0 0.9 0.0 0.00
2 7 4 oct sat 90.6 43.7 686.9 6.7 14.6 33.0 1.3 0.0 0.00
3 8 6 mar fri 91.7 33.3 77.5 9.0 8.3 97.0 4.0 0.2 0.00
4 8 6 mar sun 89.3 51.3 102.2 9.6 11.4 99.0 1.8 0.0 0.00
... ... ... ... ... ... ... ... ... ... ... ... ... ...
512 4 3 aug sun 81.6 56.7 665.6 1.9 27.8 32.0 2.7 0.0 6.44
513 2 4 aug sun 81.6 56.7 665.6 1.9 21.9 71.0 5.8 0.0 54.29
514 7 4 aug sun 81.6 56.7 665.6 1.9 21.2 70.0 6.7 0.0 11.16
515 1 4 aug sat 94.4 146.0 614.7 11.3 25.6 42.0 4.0 0.0 0.00
516 6 3 nov tue 79.5 3.0 106.7 1.1 11.8 31.0 4.5 0.0 0.00

517 rows × 13 columns

In [3]:
fires_df.info()
#Nie ma brakujących wartości

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 517 entries, 0 to 516
Data columns (total 13 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   X       517 non-null    int64  
 1   Y       517 non-null    int64  
 2   month   517 non-null    object 
 3   day     517 non-null    object 
 4   FFMC    517 non-null    float64
 5   DMC     517 non-null    float64
 6   DC      517 non-null    float64
 7   ISI     517 non-null    float64
 8   temp    517 non-null    float64
 9   RH      517 non-null    float64
 10  wind    517 non-null    float64
 11  rain    517 non-null    float64
 12  area    517 non-null    float64
dtypes: float64(9), int64(2), object(2)
memory usage: 48.5+ KB
In [4]:
fires_df.describe()
#DC ma dużą wariancję
Out[4]:
X Y FFMC DMC DC ISI temp RH wind rain area
count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000
mean 4.669246 4.299807 90.644681 110.872340 547.940039 9.021663 18.889168 44.288201 4.017602 0.021663 12.847292
std 2.313778 1.229900 5.520111 64.046482 248.066192 4.559477 5.806625 16.317469 1.791653 0.295959 63.655818
min 1.000000 2.000000 18.700000 1.100000 7.900000 0.000000 2.200000 15.000000 0.400000 0.000000 0.000000
25% 3.000000 4.000000 90.200000 68.600000 437.700000 6.500000 15.500000 33.000000 2.700000 0.000000 0.000000
50% 4.000000 4.000000 91.600000 108.300000 664.200000 8.400000 19.300000 42.000000 4.000000 0.000000 0.520000
75% 7.000000 5.000000 92.900000 142.400000 713.900000 10.800000 22.800000 53.000000 4.900000 0.000000 6.570000
max 9.000000 9.000000 96.200000 291.300000 860.600000 56.100000 33.300000 100.000000 9.400000 6.400000 1090.840000
In [5]:
fires_df.hist(bins = 40, figsize=(18, 12))
plt.show()
#ISI, RH, temp, wind bliskie rozkładowi normalnemu
#rain, area w większości wartości bliskie 0
In [6]:
sns.pairplot(fires_df, y_vars="FFMC", x_vars=fires_df.columns.values[:5])
sns.pairplot(fires_df, y_vars="FFMC", x_vars=fires_df.columns.values[5:10])
sns.pairplot(fires_df, y_vars="FFMC", x_vars=fires_df.columns.values[10:])
#zależność FFMC i ISI
Out[6]:
<seaborn.axisgrid.PairGrid at 0x16ae05d0>
In [7]:
sns.pairplot(fires_df, y_vars="DMC", x_vars=fires_df.columns.values[:5])
sns.pairplot(fires_df, y_vars="DMC", x_vars=fires_df.columns.values[5:10])
sns.pairplot(fires_df, y_vars="DMC", x_vars=fires_df.columns.values[10:])
#silna zależność DMC i DC i lekka DMC i ISI
Out[7]:
<seaborn.axisgrid.PairGrid at 0x1675bed0>
In [8]:
sns.pairplot(fires_df, y_vars="temp", x_vars=fires_df.columns.values[:5])
sns.pairplot(fires_df, y_vars="temp", x_vars=fires_df.columns.values[5:10])
sns.pairplot(fires_df, y_vars="temp", x_vars=fires_df.columns.values[10:])
#zależność temp i RH
Out[8]:
<seaborn.axisgrid.PairGrid at 0x16a6acf0>
In [9]:
sns.countplot(x="month", data=fires_df)
#sierpień i wrzesień to sezon pożarowy
Out[9]:
<AxesSubplot:xlabel='month', ylabel='count'>
In [10]:
sns.countplot(x="day", data=fires_df, order=["mon","tue","wed","thu","fri","sat","sun"])
#najwięcej pożarów w okolicach weekendu
Out[10]:
<AxesSubplot:xlabel='day', ylabel='count'>
In [11]:
pp.ProfileReport(fires_df)
#dane mają 4 zduplikowane rekordy

#największą wadą jest długi czas potrzebny na wygenerowanie raportu z dużej ilości danych
#dużo informacji, które mogą nas nie interesować
Out[11]: